What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Programming language
Bart Selman mainly investigates Satisfiability, Algorithm, Theoretical computer science, WalkSAT and Artificial intelligence.
His studies deal with areas such as Computational complexity theory and Management science as well as Satisfiability.
The Algorithm study combines topics in areas such as Domain, Initialization, Scheduling and Benchmark.
His Theoretical computer science research is multidisciplinary, incorporating perspectives in Inference, Computational problem, Graphplan and Knowledge representation and reasoning.
The various areas that Bart Selman examines in his Artificial intelligence study include Machine learning, Maximum-entropy Markov model and Computer vision.
His Local search research includes themes of Simulated annealing and Local search.
His most cited work include:
- A new method for solving hard satisfiability problems (1204 citations)
- Referral Web: combining social networks and collaborative filtering (946 citations)
- Pushing the envelope: planning, propositional logic, and stochastic search (826 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Algorithm, Theoretical computer science, Artificial intelligence, Mathematical optimization and Satisfiability.
Many of his research projects under Algorithm are closely connected to WalkSAT with WalkSAT, tying the diverse disciplines of science together.
His work carried out in the field of Theoretical computer science brings together such families of science as Combinatorial search, Message passing, Inference, Satplan and Solver.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Task and Computer vision.
His study explores the link between Mathematical optimization and topics such as Constraint satisfaction problem that cross with problems in Computational problem and Search algorithm.
His Satisfiability research incorporates elements of Domain, Distribution and Local search.
He most often published in these fields:
- Algorithm (25.00%)
- Theoretical computer science (23.53%)
- Artificial intelligence (23.16%)
What were the highlights of his more recent work (between 2011-2021)?
- Artificial intelligence (23.16%)
- Algorithm (25.00%)
- Mathematical optimization (18.75%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Algorithm, Mathematical optimization, Sequence and Theoretical computer science.
His studies link Machine learning with Artificial intelligence.
The Decoding methods research Bart Selman does as part of his general Algorithm study is frequently linked to other disciplines of science, such as Exponential growth, therefore creating a link between diverse domains of science.
Bart Selman has researched Mathematical optimization in several fields, including Computation and Set.
The study incorporates disciplines such as Combinatorial search and Combinatorial optimization in addition to Theoretical computer science.
Bart Selman has included themes like Programming language and Satisfiability in his Range study.
Between 2011 and 2021, his most popular works were:
- Unstructured human activity detection from RGBD images (401 citations)
- Understanding Batch Normalization (164 citations)
- Low-density Parity Constraints for Hashing-Based Discrete Integration (58 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Programming language
His primary areas of study are Artificial intelligence, Algorithm, Exponential growth, Theoretical computer science and Hash function.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Set.
His Algorithm research integrates issues from Initialization, Deep learning and Maxima and minima.
His Theoretical computer science study combines topics in areas such as Inference, Scalability and Combinatorial optimization.
Bart Selman studied Hash function and Graphical model that intersect with Computation, Curse of dimensionality, Randomized algorithm and Model selection.
His Mathematical optimization study focuses on Simulated annealing in particular.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
